Method _do

pymoo/operators/control.py:81–133

(self, N, random_state=None)

        self.eps = 0.05

    def _do(self, N, random_state=None):
        params = self.params
        pop = self.data.get("pop")

        # make sure that for each parameter a value exists - if not simply set it randomly
        for name, param in params.items():
            is_none = np.where(pop.get(name) == None)[0]  # noqa: E711
            if len(is_none) > 0:
                pop[is_none].set(
                    name, param.sample(len(is_none), random_state=random_state)
                )

        selection = AgeBasedTournamentSelection()

        crossover = {
            Binary: UX(),
            Real: SBX(),
            Integer: SBX(vtype=float, repair=RoundingRepair()),
            Choice: UX(),
        }

        mutation = {
            Binary: BFM(),
            Real: PM(),
            Integer: PM(vtype=float, repair=RoundingRepair()),
            Choice: ChoiceRandomMutation(),
        }

        mating = MixedVariableMating(
            crossover=crossover,
            mutation=mutation,
            eliminate_duplicates=NoDuplicateElimination(),
        )

        problem = Problem(vars=params)

        parents = selection(problem, pop, N, n_parents=2, random_state=random_state)
        parents = [
            [
                Individual(X={key: parent.get(key) for key in params})
                for parent in mating
            ]
            for mating in parents
        ]

        off = mating(problem, parents, N, parents=True, random_state=random_state)

        Xp = off.get("X")
        ret = {
            param: np.array([Xp[i][param] for i in range(len(Xp))]) for param in params
        }

        return ret


class AgeBasedTournamentSelection(TournamentSelection):